Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients

October 10, 2024 updated by: European Institute of Oncology

Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients: the Study Protocol

Background: Emerging evidence indicates that patients with advanced cancer, such as those with MBC, often exhibit significant levels of nonadherence to oral anticancer treatments. Leveraging of the machine learning models in clinical practice enables the provision of personalized predictions on medication adherence for individual patients, thereby supporting adherence and facilitating targeted interventions.

Objective: The current protocol aims to assess the efficacy of the DSS, a web-based solution named TREAT (TREatment Adherence SupporT), and a machine learning web application in promoting adherence to oral anticancer treatments within a sample of MBC patients.

Methods and Design: This protocol is part of a project titled "Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients" (Tracking Number 65080791). A sample of 100 MBC patients is enrolled consecutively and admitted to the Division of Medical Senology of the European Institute of Oncology. 50 MBC patients receive the DSS for three months (experimental group), while 50 MBC patients not subjected to the intervention receive standard medical advice (control group). The protocol foresees three assessment time points: T1 (1-Month), T2 (2-Month), and T3 (3-Month). At each time point, participants fill out a set of self-reports evaluating adherence, clinical, psychological, and QoL variables.

Conclusions: our results will inform about the effectiveness of the DSS and risk-predictive models in fostering adherence to oral anticancer treatments in MBC patients.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Metastatic breast cancer (MBC) represents an incurable condition wherein pharmacological interventions are directed towards deferring disease progression and alleviating symptoms, thereby extending survival rates and preserving the quality of life (QoL) and psychological well-being. Clinical advancements in anticancer treatments have notably augmented survival rates among MBC patients. However, accruing evidence reported that adherence to medications is a critical issue in the disease trajectory of breast cancer patients, particularly in the context of oral anticancer treatments (OATs). Emerging evidence indicates that patients with advanced cancer, such as those with MBC, often exhibit significant levels of nonadherence. MBC patients encounter various barriers to the daily management of OATs, including emotional and physical distress associated with side effects, dosage variations, treatment interruptions, and a lack of disease-related knowledge. Prediction models for adherence have been previously developed and tested across diverse scenarios and diseases. Evidence suggested that leveraging of the machine learning models in clinical practice enables the provision of personalized predictions on medication adherence for individual patients, thereby supporting adherence and facilitating targeted interventions. Even so, existing studies have yet to systematically address medication adherence among MBC patients by designing and implementing a decision support system (DSS) that integrates risk predictive models alongside educational and training tools.

The current protocol aims to assess the efficacy of the DSS, a web-based solution named TREAT (TREatment Adherence SupporT), and a machine learning web application in promoting adherence to oral anticancer treatments within a sample of MBC patients. This protocol is part of a project titled "Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients" (Tracking Number 65080791). The overarching goal of this project is to develop a predictive model of nonadherence, an associated DSS, and guidelines to enhance patient engagement and therapy adherence among MBC patients.

The web-based DSS was developed in the first year of the Pfizer Project (65080791) using a patient-centric approach and comprises four sections: i) Metastatic Breast Cancer; ii) Adherence to Cancer Therapies; iii) Promoting Adherence; iv) My Adherence Diary. Moreover, a machine learning web-based application was designed to focus on predicting patients' risk factors for adherence to anticancer treatment, specifically considering physical status, comorbid conditions, and short- and long-term side effects. This machine learning web-based application was developed through a retrospective study employing physiological, clinical, and quality of life data available in the European Institute of Oncology (Milan, Italy) (R1595/21-IEO 1704). Specifically, multi-modal retrospective data has been retrieved from the Patient Electronic Health Records (EHR) using natural language processing (NLP) in a sample of 2.750 MBC patients (from 2010 to 2020).

Methods/Design

Main objectives

Evaluating the effectiveness of the DSS web-based solution and machine learning web application (TREAT - "TREatment Adherence SupporT") in fostering adherence to oral anticancer treatments within a cohort of 100 Metastatic Breast Cancer (MBC) patients over a three-month period. Adherence is assessed by calculating the number of pills taken divided by the prescribed amount.

Secondary Objectives

Identify clinical factors (comorbidities, pain presence, tumor type, treatment type), psychological parameters (personality traits, anxiety, depression, self-efficacy for coping with cancer, sense of coherence, and risk perception), and QoL variables that serve as predictors for patients' adherence to OATs. These predictors are utilized to assess nonadherence to OATs among MBC patients and enhance the initial version of a machine learning model developed in the retrospective study (R1595/21-IEO 1704). Data for the secondary endpoints are collected using the European Organization for Research and Treatment of Cancer Quality of Life questionnaire (EORTC-QLQ-C30), the European Organization for Research and Treatment of Cancer 23-item Breast Cancer-specific Questionnaire (EORTC-QLQ-BR23), and the Brief Pain Inventory (BPI). Furthermore, to evaluate psychological variables, the following measures are used: the State-Trait Anxiety Inventory (STAI-Y), the Beck Depression Inventory-II (BDI-II), the Big Five Inventory (BFI), the Cancer Behavior Inventory CBI Short form (CBI-B/I), the Sense of Coherence (SOC-13), and Risk Perception (utilizing two Visual Analog Scales).

Trial Duration and Study Design

The study is designed as a 3-month randomized controlled study conducted at the European Institute of Oncology (IEO). More specifically, a sample of 100 patients is enrolled consecutively and admitted to the Division of Medical Senology with an MBC diagnosis. Patients who signed the informed consent are given a unique identifier and assigned to either the control or intervention arm in a 1:1 ratio. Earliest, the system asks to confirm all inclusion and exclusion criteria. Then, an independent researcher generates a random sequence using the statistical language R (R Core Team 2020).

Experimental Group - TREAT (TREatment Adherence SupporT): 50 MBC patients receive the DSS for three months. Patients are instructed to use the DSS ad libitum. Further, Patients are explicitly informed that TREAT does not replace clinical consultations, but it is designed to assist in managing oral treatment and enhancing adherence through education based on evidence-based information. Control Group: 50 MBC patients not subjected to the intervention receive standard medical advice.

The protocol foresees three assessment time points: T1 (1-Month), T2 (2-Month), and T3 (3-Month). At the baseline (T0), all patients fill out validated questionnaires to measure adherence, clinical, psychological, and QoL variables. The expected time to complete all the given questionnaires at baseline is approximately 40 minutes. Furthermore, all patients have to fill a weekly adherence medication diary for three months. Each month, all participants receive a brief telephone interview in which they are monitored for compliance with the research protocol. At T1, T2, and T3, all behavioral, psychological, and QoL measures are filled out, and an interview (online or vis-à-vis) is performed. Variables that are not sensitive to change, such as personality and anxiety trait, are collected only at T0.

Study Type

Interventional

Enrollment (Actual)

94

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Italy
    • MI
      • Milan, MI, Italy, 20141
        • European Institute fo Oncology

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Patients > 18 years-old;
  • Having a metastatic breast cancer diagnosis;
  • Taking oral treatment intervention for metastatic breast cancer;
  • Patients with internet access and a personal smartphone or tablet;
  • Patients who have read and signed the informed consent.

Exclusion Criteria:

  • Presence of primary psychiatric or neurological conditions;
  • Patients who refused to sign the informed consent.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Other
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Experimental Group
50 MBC patients receive the DSS for three months. Patients are instructed to use the DSS ad libitum.
Device: Decision Support System

TREAT (TREatment Adherence SupporT) is a web-based DSS that comprises four sections:

i) Metastatic Breast Cancer: contains information about MBC and its physical and psychological consequences;

ii) Adherence to Cancer Therapies: contains information about adherence in the cancer population;

iii) Promoting Adherence: contains information about resources, barriers, and available interventions used to foster adherence;

iv) My Adherence Diary.
No Intervention: Control Group
50 MBC patients not subjected to the intervention receive standard medical advice.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Decision Support System Effectiveness
Time Frame: 3 Months
Evaluating the effectiveness of the DSS web-based solution and machine learning web application (TREAT - "TREatment Adherence SupporT") in fostering adherence to oral anticancer treatments
3 Months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Clinical, Psychological and Quality of Life Predictors of Adherence
Time Frame: 3 Months
Identify clinical factors (comorbidities, pain presence, tumor type, treatment type), psychological parameters (personality traits, anxiety, depression, self-efficacy for coping with cancer and sense of coherence), and QoL variables that serve as predictors for patients' adherence to OATs.
3 Months
Psychological Predictors of Adherence
Time Frame: 3 Months
Evaluate risk perception using visual analogue scale that serve as predictors for patients' adherence to OATs.
3 Months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

European Institute of Oncology

Collaborators

Pfizer

Investigators

  • Principal Investigator: Gabriella pravettoni, PhD, Istituto Europeo di Oncologia

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 3, 2023

Primary Completion (Actual)

February 15, 2024

Study Completion (Actual)

May 15, 2024

Study Registration Dates

First Submitted

November 30, 2023

First Submitted That Met QC Criteria

November 30, 2023

First Posted (Actual)

December 7, 2023

Study Record Updates

Last Update Posted (Actual)

October 15, 2024

Last Update Submitted That Met QC Criteria

October 10, 2024

Last Verified

October 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IEO1907

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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